CN107742418A - A kind of urban expressway traffic congestion status and stifled point position automatic identifying method - Google Patents
A kind of urban expressway traffic congestion status and stifled point position automatic identifying method Download PDFInfo
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Abstract
The invention discloses a kind of urban expressway traffic congestion status and stifled point position automatic identifying method, comprise the following steps：1) threshold value of each monitoring section divided lane traffic parameter is set；2) automobile image/image in acquisition field of view, the traffic data information of conversion generation divided lane, and the traffic data information of divided lane is transmitted to transport information processing server；3) transport information processing server carries out congestion status and stifled point position analysis using real time traffic data information and traffic parameter threshold data storehouse；4) set according to user, analysis result and corresponding suggestion are forwarded to user terminal.The present invention can judge the section position and place track that goods drops, maliciously jumped a queue, failure/traffic events such as parking offense behavior or traffic accident occur rapidly in the very first time, vehicle supervision department is contributed to reasonably to tackle in time, follow-up more serious traffic congestion is avoided, reduces second accident rate.
Description
Technical field
The present invention relates to combine road monitoring, graphical analysis and traffic behavior judge etc. in a kind of city expressway of one
Traffic congestion state and stifled point position automatic identifying method, belong to traffic congestion monitoring technical field.
Background technology
As the quick increase of China's city automobile recoverable amount, traffic congestion situation are also more serious.Wherein, no doubt there is friendship
Logical demand is big and the fundamental cause of road infrastructure supply relative deficiency, still, there is also vehicle " snail row ", failure/
Parking offense, maliciously jump a queue, goods drops, traffic accident etc. is caused by " traffic events " " sporadic traffic congestion ".
Due to the regularity of " sporadic traffic congestion ", periodically not as " often hair property traffic congestion " significantly, it is difficult to prejudge
Or take precautions against in advance；Meanwhile " sporadic traffic congestion " coverage of significant proportion is smaller, the duration is shorter, management must
The property wanted seems deficiency, it is difficult to causes the common concern of vehicle supervision department.However, correlative study shows, some seem small
Traffic events, because mishandling or reply is not prompt enough, finally but cause the serious traffic congestion of large area.It is it can be seen that right
For vehicle supervision department, road traffic condition (especially traffic congestion state and stifled point position) is learned accurately and in time
It is very necessary.
Traditional urban road traffic congestion monitoring is mainly real by personal monitoring, emergency call or induction coil detector
It is existing.Personal monitoring is less efficient and easy slips, it is difficult to tackles metropolitan complicated road network；Emergency call it is ageing not
Accuracy deficiency that is good, and judging congestion level；Induction coil detector can only monitor part traffic data, system installation
When need to destroy road surface and follow-up maintenance guarantee and bother, larger use limitation be present.In recent years, with HD video information
Collection and the fast development of transmission technology, big data treatment technology, the urban road traffic congestion state based on HD video is certainly
Dynamic monitoring technology is in the ascendant.
The automatic monitoring of urban road traffic congestion state, it is according to real-time traffic characteristic parameters, feature is joined rapidly
Number judges, and detects the presence of traffic congestion state, and the traffic congestion state to monitoring carries out early warning or alarm.This
Contribute to vehicle supervision department to take counter-measure in time, reduce the order of severity of traffic congestion to greatest extent and influence model
Enclose.
The existing urban road traffic congestion state automatic monitoring method based on (high definition) video, generally setting is handed over first
The threshold value of logical parameter (such as occupation rate, average speed or link traffic flow), when the real-time traffic parameter that video camera obtains is one
Timing is long and to a certain extent more than after above-mentioned threshold value, forms the judgement and suggestion to traffic congestion level, and trigger alarming machine
System.
Because the relation between the main parameter of road traffic is more complicated, traffic characteristic (speed, the change of driving vehicle on road
Road situation) also vary, it is existing, based on monitoring section totality traffic parameter congestion evaluation algorithm exist very it is big not
Foot.Cast anchor accident for example, there is vehicle on the inside of the road of certain (unidirectional) three lanes city expressway, cause disabled vehicles institute
The more cars in track are lined up.Because the speed in other two tracks is temporarily not affected by too big influence, the occupation rate in section does not also go out
Now vary widely, the automatic identifying method based on average speed or section occupation rate can not make the early warning of traffic congestion.However,
The subsequent section will engender traffic congestion and the greater risk for triggering second accident be present.Till that time, system is judged again
Traffic congestion is then late.
And for example, when a certain city expressway is absorbed in traffic congestion along the considerably long section in road, many places video along the line
Monitoring system can all show that real-time traffic parameter exceedes threshold value and sends alarm.Now, which limited police strength should send to actually
A little sections just turn into very big problem.Once getting the wrong sow by the ear, police strength continues to transfer by the good opportunity for control traffic congestion of staggering the time.Can
See, along the line all in the traffic congestion judgment mechanism of alarm, not too big realistic meaning.
The content of the invention
The present invention is technological deficiency existing for overcoming existing traffic congestion monitoring method, proposes a kind of urban expressway traffic
Congestion status and stifled point position automatic identifying method, can be automatically pre- when (or will occur) congestion occurs for city expressway
Police, alarm and prompt a stifled point position, to assist vehicle supervision department to solve judging traffic congestion state and tackling selection scheme
Etc. the problem of existing, reduce socioeconomic impact caused by traffic congestion.
In order to solve the above technical problems, the invention provides a kind of urban expressway traffic congestion and stifled point position certainly
Dynamic recognition methods, comprises the following steps：
1) threshold value setting is carried out to the traffic parameter of each monitoring each divided lane in section, traffic parameter includes being located at peak
The normally travel speed of vehicle, space occupancy, lane change frequency and direction, the volume of traffic in phase, flat peak phase；
2) by the automobile image information being arranged in the camera acquisition visual field by monitoring road, and change generation and divide car
Road real time traffic data information, by the divided lane real time traffic data information transfer to transport information processing server, this is real-time
Traffic data information includes the translational speed of vehicle in visual field, in visual field between the space occupancy in each bar track and visual field inside lane
Lane change frequency, it is described change Dow Jones index vehicle center line cross lane line；
3) transport information processing server according to the real time traffic data information and traffic parameter threshold value in the monitoring section simultaneously
The real time traffic data information and the traffic parameter threshold value of relevant road segments that association monitoring section upstream and downstream section video camera obtains are entered
Row congestion and stifled point position analysis；
4) set according to user, analysis result, realtime graphic and corresponding suggestion are sent to user terminal.
Further, congestion judgement is carried out according to real time traffic data information and traffic parameter threshold value in step 3),
Judge including " snail row " behavior judgement, parking behavior and lane change frequency judges；
If peak period Vehicle Speed is less than the average speed of vehicle or flat peak phase vehicle traveling speed in camera field of view
Degree is less than normally travel threshold speed, i.e.,：
Work as Mean-vt>0.4×vDWhen：
vtk≤α×Mean-vtPeak period (1)
vtk≤β×vDFlat peak period (2)
It is considered as " snail row " behavior；
Wherein, vtkFor time t when vehicle k travel speed, Mean-vtFor time t when camera field of view in vehicle it is flat
Equal speed, vDFor the design speed in the through street section, α and β are to control the coefficient of management Stringency, can combine traffic
The historical data of engineering science model or step 1) is configured；
If the average speed of vehicle in camera field of view：
Mean-vt≤0.4×vD (3)
It is considered as wagon flow integrally to walk or drive slowly；
If right ahead road " space occupancy " is zero and its travel speed is continuously zero in certain time length, i.e.,：
vtk=v(t+Δtk)=0 (4)
It is considered as parking behavior；
The lane change frequency judges to include：
When on certain track：
ΣCmn≥γ×Tm (5)
It is considered as lane change frequency height；
Wherein, CmnFor number of the vehicle in time interval Δ t from track m lane changes to adjacent lane n, Σ CmnFor from track m
Lane change is to all numbers for closing on track behavior, TmFor the volume of traffic in the arrival upstream edge boundary line in m tracks in time interval, γ is
Coefficient, the volume of traffic are the vehicle number for crossing camera field of view upstream edge boundary line in time interval Δ t on m tracks.
Further, the threshold value in step 1) sets and included：Collect the history number of each monitoring section divided lane traffic parameter
According to, and according to period residing for the section, whether be that working day, season, weather conditions are handled the historical data being collected into,
Threshold value setting is carried out to each monitoring road section traffic volume parameter according to the data after the processing and establishes historical data base.
Further, step 1) and 2) in each bar track space occupancy to be a certain instantaneous, the car travelled on section
Total floor space accounts for the percentage of the section gross area.
Further, the congestion analysis of causes of step 3) includes：Vehicle " snail row ", maliciously jump a queue, goods drops, failure/
Parking offense behavior, traffic accident.
Further, it is that city expressway includes overpass, tunnel, ground floor and all kinds of interwoven regions to monitor section, described
Interwoven region includes up and down/disengaging ring road and the main line junction in plane interwoven region, overpass or tunnel.
Further, congestion and stifled point position are analyzed according to " snail row " behavior of divided lane and parking behavior in step 3)
Put including：
When the vehicle that snail row or parking on any track of visual field or multilane be present is for a long time in the visual field, if most forward
Vehicle is not up to visual field downstream side boundary line, then is judged as that the track has failure/parking offense behavior or traffic accident；If most lean on
Vehicle in front has reached visual field downstream side boundary line, then is judged as that the track downstream direction has failure/parking offense behavior or traffic
Accident；
The vehicle is defined as the duration threshold for the time that the vehicle is located in visual field being more than setting in visual field for a long time
Value, the duration threshold value are configured according to the Stringency of management.
Further, in step 3) according to vehicle lane change frequency in visual field and Orientation congestion and a stifled point position,
Including：
When vehicle lane change frequency exceedes the lane change frequency threshold of setting in visual field, according to vehicle lane change direction and each track
Lane change frequency is avoided track to vehicle and judged, and be judged as vehicle avoid track downstream road section occur goods drop,
Failure/parking offense behavior or traffic accident, the real-time friendship that transport information processing server association downstream road section video camera obtains
Logical data message, is determined.
Further, congestion and stifled point position are analyzed according to space occupancy in visual field in step 3), including：
When the vehicle translational speed in visual field is not less than normally travel speed, but the real-time space occupancy in the section is less than
During the space occupancy threshold value of setting, then be judged as the visual field upstream section occur vehicle " snail row ", goods drop, failure/
Parking offense behavior or traffic accident, the real time traffic data that transport information processing server association upstream section video camera obtains
Information further determines that；
If the average speed in the camera field of view of upstream is less than normally travel speed, real-time space occupancy is higher than space
It occupation rate threshold value, then can determine whether that " blind area " section of point between two groups of video cameras occurs for traffic events；If upstream images
Real-time space occupancy in machine visual field is also less than space occupancy threshold value, then judges that traffic events occur point and are still located at the upstream
The upstream section of video camera, repeat the above steps to determine that the specific generation position of point occurs for traffic events.
Further, in step 4)：
User sets the vehicle-state duration length given threshold included to each track in visual field；
Analysis result includes：Monitor the space occupancy in section and its corresponding traffic congestion level, traffic thing in visual field
Section where the track and the track of the part order of severity and generation traffic events.
It is recommended that including：Manual analysis in real time or history image, notice, warning, punishment vehicles peccancy, notice or warning upstream
The driving vehicle in section.
Beneficial effect：The present invention compared with prior art, can gather around to the especially sporadic traffic of traffic congestion state automatically
Stifled state implements monitoring and identification；Carry out transport information state acquisition automatically based on high-definition video signal, be able in the very first time
The rapid section position for judging that goods drops, maliciously jumped a queue, failure/traffic events such as parking offense behavior or traffic accident occur
Put and place track；Traffic congestion state and stifled dot position information carry out early warning, alarm hair using push mode and forward mode
Cloth.Relative to prior art, the specific space bit for causing the traffic events of congestion to occur can be more accurately and timely found
Put, contribute to vehicle supervision department reasonably to tackle in time, such as notice, warning and punishment vehicles peccancy, send in time
Go out the driving vehicle of police strength or other support forces, notice or warning upstream section, etc..
Brief description of the drawings
Fig. 1 is the realization stream of urban expressway traffic congestion status provided by the invention and stifled point position automatic identifying method
Cheng Tu；
Fig. 2 is camera field of view schematic diagram provided by the invention；
Fig. 3 is urban expressway traffic congestion status provided by the invention and the logic judgment of stifled point position automatic identification
Figure.
Embodiment
The present invention is further described below in conjunction with the accompanying drawings.
Fig. 1 shows the realization of the urban expressway traffic congestion status and stifled point position automatic identifying method of the present embodiment
Flow chart, specifically include following step：
In step s 11, collect historical data or set each monitoring section divided lane to hand over the method for traffic engineering
The threshold value of logical parameter：
The traffic parameter of acquisition monitoring section divided lane includes：Average speed, space occupancy in intervals,
Lane change frequency and direction, the volume of traffic；According to the residing period, whether the factor such as working day, season, weather data are classified,
The statistical dispositions such as denoising, obtain the threshold value of each road section traffic volume parameter under each subdivision classification and establish historical data base.Collecting
Before enough historical datas, the method that existing traffic engineering can be used, rule of thumb data and the result of theory deduction
The threshold value of above-mentioned traffic parameter is set.
In step s 12, automobile image/figure by being arranged in the high-definition camera acquisition field of view by monitoring road
Picture, the traffic data information of conversion generation divided lane, and the traffic data information of divided lane is transmitted to transport information processing and taken
Business device；
In step s 12, traffic data information includes the translational speed of driving vehicle in visual field, to realize that travel speed shows
Writing " snail row " behavior less than other most of vehicles, accident/failure/parking offense behavior and malice and jumping a queue causes by car of jumping a queue
The behavior such as bring to a halt, find and be significantly lower than in peak period travel speed significantly lower than other vehicles or in flat peak phase travel speed
The target of normally travel threshold speed, vehicle " snail row " velocity estimated are as follows：
If vehicle is significantly lower than other vehicles or flat peak phase travel speed in camera field of view in peak period travel speed
Less than normally travel threshold speed, i.e.,：
Work as Mean-vt>0.4×vDWhen：
vtk≤α×Mean-vtPeak period (1)
vtk≤β×vDFlat peak period (2)
It is considered as " snail row " behavior.
Wherein, vtkFor time t when vehicle k travel speed, Mean-vtFor time t when camera field of view in vehicle it is flat
Equal speed, vDFor the design speed in the through street section, α and β are to control the coefficient of management Stringency, can combine traffic
Engineering science model or step S11 historical data are configured, and it is respectively 0.8 and 0.7 that α and β values are given tacit consent in the present embodiment.
If the average speed of vehicle in camera field of view：
Mean-vt≤0.4×vD (3)
It is considered as wagon flow integrally to walk or drive slowly.
If right ahead the coast is clear, i.e. 5 meters of " space occupancies " apart from interior road in front are zero, and it is travelled
Speed is continuously zero in certain time length, i.e.,：
vtk=v(t+Δtk)=0 (4)
It is considered as parking behavior.
" space occupancy " of the prior art is defined as：A certain moment t, the vehicle total length Zhan Gai roads travelled on section
The percentage of segment length, i.e. space occupancy (%) Ot=(Σ Length-car)/Length-road；In step s 12, originally
" space occupancy " in embodiment is newly defined as：A certain moment t, the total floor space of vehicle travelled on section account for the section
The percentage of the gross area, i.e. space occupancy (%) Ot'=(Σ Area-car)/Area-road.This is allowed for, very
Crowded section, the quantity of vehicle queue queue are possible to exceed number of track-lines, for example, the through street of unidirectional three lanes, may go out
Existing four vehicle queue queues, the space occupancy that the present embodiment hereinafter refers to is the space occupancy after redefining
(%) Ot'=(Σ Area-car)/Area-road.
In step s 12, traffic data information also includes " the lane change frequency " or Travel vehicle in visual field between each adjacent lane
The frequency that transverse direction significantly moves.When on certain track：
ΣCmn≥γ×Tm； (5)
It is considered as and frequent lane change behavior is present.
Wherein：CmnFor number of the vehicle in time interval Δ t from track m lane changes to adjacent lane n, m, n are expressed as track
The natural number of numbering, m, n ∈ [1,2 ... ... N]；ΣCmnFor from track m lane changes to all numbers for closing on track behavior；γ is
Coefficient, to control the Stringency of monitoring, it is proposed that γ values 0.8；TmFor the arrival upstream edge boundary line in m tracks in time interval
The volume of traffic, the time interval in units of minute, wherein, vehicle center line, which crosses lane line, is considered as change lane, should
The volume of traffic is defined as crossing the vehicle number in camera field of view upstream edge boundary line on m tracks in time interval Δ t.
In step s 13, transport information processing server carries out congestion status analysis, specific side according to traffic data information
The statement that method sees below to Fig. 3 contents.
In step S14, set according to user, analysis result and corresponding suggestion are forwarded to user terminal.Analysis result
Including：Monitor the space occupancy and its corresponding traffic congestion level, the possibility type of the traffic events occurred, hair in section
The section in raw traffic events track and place, it is corresponding to suggest including：Notice, warning and punishment vehicles peccancy, send police strength in time
Or other support forces, the driving vehicle in notice or warning upstream section, the traffic congestion level in the present embodiment specifically calculate
According to the existing formula of traffic engineering.
Fig. 2 shows the camera field of view schematic diagram of the present embodiment.
Wherein, l represents the section that the visual field of current camera is covered, lOnRepresent that current camera monitors the upper of section
Swim section, lUnderRepresent that current camera monitors the downstream road section in section；X represents the upstream boundary in current camera monitoring section
Line, Y represent the downstream side boundary line in current camera monitoring section.
Fig. 3 shows the urban expressway traffic congestion status of the present embodiment and the logic judgment of stifled point position automatic identification
Figure, for purposes of illustration only, only giving part related to the present embodiment in figure.
According to the information of the camera acquisition at section, it is first determined whether having low speed or the vehicle of parking for a long time
In visual field, the section includes overpass, tunnel and ground floor, and low speed or parking vehicle are defined as in visual field for a long time
It is judged as low speed, " snail row " or the vehicle that stops be located at time in visual field and be more than the duration threshold value set, the duration threshold value
It is configured according to the Stringency of management, span is usually 5~30 seconds：
As fruit part track has vehicle most forward on such vehicle and direction of advance to be not up to visual field downstream side boundary line Y,
Then judge failure/parking offense behavior or traffic accident, and the traffic events occur at place track (judging A), can incite somebody to action
Realtime graphic/video is passed back for manual analysis.
As there is such vehicle in fruit part track and vehicle most forward in a forward direction has reached visual field downstream side boundary line
Y, then judge failure/parking offense behavior or traffic accident, and the downstream direction in place track occurs for the traffic events
(judging B), neighbouring spherical camera may be guided and go to direction progress act of violating regulations or accident confirmation.
If there are low speed or stopping vehicle in all tracks and vehicle most forward in a forward direction is not up under visual field
Boundary line Y is swum, then judges failure/parking offense behavior or traffic accident, and the traffic events have had a strong impact on all cars
Road (judges A), immediately passes realtime graphic/video for manual analysis back.
If there are low speed or stopping vehicle in all tracks and vehicle most forward in a forward direction is had been lined up to visual field
Downstream side boundary line Y, then judge that more serious traffic congestion state occurs in downstream road section.Now, if in the range of certain time
It is lasting frequently unidirectional transverse shifting or lane change occur, then judge stock thing drop, failure/parking offense behavior or traffic thing
Therefore and the traffic events occur to avoid the downstream direction (judging B) in track in vehicle；If do not continue in the range of certain time
There is frequently unidirectional transverse shifting or lane change, then judge that traffic events appear in downstream direction remotely, or the traffic occurred
Congestion is not due to traffic events cause, but " often hair property traffic congestion ", that is, judges C.When judging B or C, continue to take the photograph downstream
Camera collection information analyzed, such as guide neighbouring spherical camera go to the direction carry out act of violating regulations or accident it is true
Recognize, to further determine that the specific generation position of traffic events.
If the Vehicle Speed in visual field is located at zone of reasonableness, but persistently occurs frequently in the range of certain time
Unidirectional transverse shifting or lane change, then judge that stock thing drops, failure/parking offense behavior or traffic accident and the event occur
Downstream direction in the track that vehicle is avoided or this track, judges B, can pass realtime graphic/video for manual analysis back.
Because the artificial blocking of traffic lights etc is not present in city expressway, traffic flow thereon belongs to continuous car
Flow, the traffic parameter and traffic behavior between upstream and downstream have more direct relation, and association upstream and downstream section video camera obtains
Traffic parameter, help to obtain more accurate, timely traffic event information.
If the Vehicle Speed in visual field is located at zone of reasonableness, but on weekdays in peak period " space is occupied
Rate " is substantially less than normal level, e.g., the section fluctuated for peak period " space occupancy " between 0.4~0.5, finds it
" space occupancy " is less than 0.3 in continuous 1 minute, then judges that the parameter is substantially less than normal level, the normal level is with each section
Threshold reference step S11 historical data base carry out value, then the upstream of visual field is likely occurred vehicle " snail row ", goods falls
Fall, failure/parking offense behavior or traffic accident；If " space occupancy " in certain time length is still significantly lower than normal water
It is flat, then visual field upstream be likely occurred goods drop, failure/parking offense behavior or traffic accident.Now, upstream road can be combined
The transport information that section video camera obtains, as the average speed in the camera field of view of upstream is relatively low and " space occupancy " is higher than threshold
Value, then it can determine whether that " blind area " section (judging D) of point between two groups of video cameras occurs for traffic events；As upstream video camera regards
" space occupancy " in is also less than threshold value, then judges to block up the upstream section (judging E) before point is still located at the video camera, can
Determine that the spherical camera that traffic events occur near the specific generation position of point and guiding goes to this to repeat the above steps
Direction carries out act of violating regulations or accident confirms.
It is located at the information of the camera acquisition at interwoven region for visual field, except foregoing judgment criterion, also needs to pay special attention to
" space occupancy " in each bar track in interwoven region, the interwoven region include the up and down/disengaging circle in plane interwoven region, overpass or tunnel
Road and main line junction.
If " space occupancy " of main line fast lane is relatively low or speed is higher, and main line kerb lane " space accounts for
Have rate " higher or speed is relatively low, above-mentioned " space occupancy " be relatively low or speed compared with Gao Jun with the historical data in step S11
Storehouse value is compared, then judge main line kerb lane or ring road occur malice jump a queue, failure/parking offense behavior or traffic accident
(judging A)；If this state continue for certain time (such as more than 30 seconds), main line kerb lane or circle can be further determined that
Road occur malice jump a queue, failure/parking offense behavior or traffic accident (judging A).
In the present embodiment, user can set and adjust the Stringency of monitoring and alarm.For example, discovery can be set
Low vehicle speeds or halted state duration length threshold, system will only to low vehicle speeds in camera field of view or
Situation when halted state duration exceedes threshold value is responded and analyzed, to avoid excessively frequently alarming and manually do
In advance.
Claims (10)
1. a kind of urban expressway traffic congestion and stifled point position automatic identifying method, it is characterised in that：Including following step
Suddenly：
1) threshold value setting is carried out to the traffic parameter of each monitoring each divided lane in section, traffic parameter includes positioned at peak period, put down
The normally travel speed of vehicle, space occupancy, lane change frequency and direction, the volume of traffic in the peak phase；
2) by the automobile image information being arranged in the camera acquisition visual field by monitoring road, and it is real to change generation divided lane
When traffic data information, by the divided lane real time traffic data information transfer to transport information processing server, the real-time traffic
Data message includes the translational speed of vehicle in visual field, the change in visual field between the space occupancy in each bar track and visual field inside lane
Road frequency, the change Dow Jones index vehicle center line cross lane line；
3) transport information processing server according to the real time traffic data information in the monitoring section and traffic parameter threshold value and associates
The real time traffic data information and the traffic parameter threshold value of relevant road segments that monitoring section upstream and downstream section video camera obtains are gathered around
Stifled situation and stifled point position analysis；
4) set according to user, analysis result, realtime graphic and corresponding suggestion are sent to user terminal.
2. a kind of urban expressway traffic congestion according to claim 1 and stifled point position automatic identifying method, its
It is characterised by：Congestion judgement is carried out according to real time traffic data information and traffic parameter threshold value in the step 3), including
" snail row " behavior judgement, parking behavior judge and lane change frequency judges；
It is low that if peak period Vehicle Speed is less than the average speed of vehicle or flat peak phase Vehicle Speed in camera field of view
In normally travel threshold speed, i.e.,：
Work as Mean-vt>0.4×vDWhen：
vtk≤α×Mean-vtPeak period (1)
vtk≤β×vDFlat peak period (2)
It is considered as " snail row " behavior；
Wherein, vtkFor time t when vehicle k travel speed, Mean-vtFor time t when camera field of view in vehicle average car
Speed, vDFor the design speed in the through street section, α and β are to control the coefficient of management Stringency, can combine traffic engineering
The historical data for learning model or step 1) is configured；
If the average speed of vehicle in camera field of view：
Mean-vt≤0.4×vD (3)
It is considered as wagon flow integrally to walk or drive slowly；
If right ahead road " space occupancy " is zero and its travel speed is continuously zero in certain time length, i.e.,：
vtk=v(t+Δtk)=0 (4)
It is considered as parking behavior；
The lane change frequency judges to include：
When on certain track：
ΣCmn≥γ×Tm (5)
It is considered as lane change frequency height；
Wherein, CmnFor number of the vehicle in time interval Δ t from track m lane changes to adjacent lane n, Σ CmnFor from track m lane changes
To all numbers for closing on track behavior, TmFor the volume of traffic in the arrival upstream edge boundary line in m tracks in time interval, γ is to be
Number, the volume of traffic are the vehicle number for crossing camera field of view upstream edge boundary line in time interval Δ t on m tracks.
3. a kind of urban expressway traffic congestion according to claim 1 and stifled point position automatic identifying method, its
It is characterised by：Threshold value in the step 1), which is set, to be included：The historical data of each monitoring section divided lane traffic parameter is collected, and
According to period residing for the section, whether be that working day, season, weather conditions are handled the historical data being collected into, according to
Data after the processing carry out threshold value setting to each monitoring road section traffic volume parameter and establish historical data base.
4. a kind of urban expressway traffic congestion according to claim 1 and stifled point position automatic identifying method, its
It is characterised by：The step 1) and 2) in each bar track space occupancy to be a certain instantaneous, the vehicle travelled on section is total
Floor space accounts for the percentage of the section gross area.
5. a kind of urban expressway traffic congestion according to claim 1 and stifled point position automatic identifying method, its
It is characterised by：The congestion analysis of causes of the step 3) includes：Vehicle " snail row ", maliciously jump a queue, goods drops, failure/violating the regulations
Parking behavior, traffic accident.
6. a kind of urban expressway traffic congestion according to claim 1 and stifled point position automatic identifying method, its
It is characterised by：The monitoring section is that city expressway includes overpass, tunnel, ground floor and all kinds of interwoven regions, the intertexture
Area includes up and down/disengaging ring road and the main line junction in plane interwoven region, overpass or tunnel.
7. a kind of urban expressway traffic congestion according to claim 2 and stifled point position automatic identifying method, its
It is characterised by：Analyzing congestion and stifled point position according to " snail row " behavior of divided lane and parking behavior in step 3) includes：
When the vehicle that snail row or parking on any track of visual field or multilane be present is for a long time in the visual field, if most forward vehicle
Not up to visual field downstream side boundary line, then it is judged as that the track has failure/parking offense behavior or traffic accident；If most forward car
Visual field downstream side boundary line is reached, has then been judged as that the track downstream direction has failure/parking offense behavior or traffic accident；
The vehicle is defined as the duration threshold value for the time that the vehicle is located in visual field being more than setting in visual field for a long time, should
Duration threshold value is configured according to the Stringency of management.
8. a kind of urban expressway traffic congestion and stifled point position automatic identifying method according to claim 2 or 7,
It is characterized in that：According to vehicle lane change frequency in visual field and Orientation congestion and stifled point position in step 3), including：
When vehicle lane change frequency exceedes the lane change frequency threshold of setting in visual field, according to vehicle lane change direction and each lane
Frequency is avoided track to vehicle and judged, and be judged as vehicle avoid track downstream road section occur goods drop, failure/
Parking offense behavior or traffic accident, the real time traffic data that transport information processing server association downstream road section video camera obtains
Information, it is determined.
9. a kind of urban expressway traffic congestion according to claim 7 and stifled point position automatic identifying method, its
It is characterised by：Congestion and stifled point position are analyzed according to space occupancy in visual field in step 3), including：
When the vehicle translational speed in visual field is not less than normally travel speed, but the real-time space occupancy in the section is less than setting
Space occupancy threshold value when, then be judged as that vehicle " snail row " occurs for the upstream section of the visual field, goods drops, failure/violating the regulations
Parking behavior or traffic accident, the real time traffic data information that transport information processing server association upstream section video camera obtains
Further determine that；
If the average speed in the camera field of view of upstream is less than normally travel speed, real-time space occupancy is occupied higher than space
It rate threshold value, then can determine whether that " blind area " section of point between two groups of video cameras occurs for traffic events；If upstream video camera regards
Real-time space occupancy in is also less than space occupancy threshold value, then judges that traffic events occur point and are still located at upstream shooting
The upstream section of machine, repeat the above steps to determine that the specific generation position of point occurs for traffic events.
10. a kind of urban expressway traffic congestion according to claim 1 and stifled point position automatic identifying method, its
It is characterised by：In the step 4)：
User sets the vehicle-state duration length given threshold included to each track in visual field；
Analysis result includes：It is tight to monitor the space occupancy in section and its corresponding traffic congestion level, traffic events in visual field
Section where the track and the track of weight degree and generation traffic events.
It is recommended that including：Manual analysis in real time or history image, notice, warning, punishment vehicles peccancy, notice or warning upstream section
Driving vehicle.
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| CN116153086A (en) * | 2023-04-21 | 2023-05-23 | 齐鲁高速公路股份有限公司 | Multi-path traffic accident and congestion detection method and system based on deep learning |
| CN116704771A (en) * | 2023-06-21 | 2023-09-05 | 中咨数据有限公司 | Real-time positioning command processing system based on traffic information of congested road section |
| CN116704771B (en) * | 2023-06-21 | 2024-01-12 | 中咨数据有限公司 | Real-time positioning command processing system based on traffic information of congested road section |
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